Classification de situations de conduite et détection des événements critiques d'un deux roues motorisé

This thesis aims to develop framework tools for analyzing and understanding the riding of Powered Two Wheelers (PTW). Experiments are conducted using instrumented PTW in real context including both normal (naturalistic) riding behaviors and critical riding behaviors (near fall and fall). The two objectives of this thesis are the riding patterns classification and critical riding events detection. In the first part of this thesis, a machine-learning framework is used for riding pattern recognition problem. Therefore, this problem is formulated as a classification task to identify the class of riding patterns. The approaches developed in this context have shown the interest to take into account the temporal aspect of the data in PTW riding. Moreover, we have shown the effectiveness of hidden Markov models for such problem. The second part of this thesis focuses on the development of the off-line detection and classification of critical riding events tools and the on-line fall detection. The problem of detection and classification of critical riding events has been performed towards two steps: (1) the segmentation step, where the multidimensional time of data were modeled and segmented by using a mixture model with quadratic logistic proportions; (2) the classification step, which consists in using a pattern recognition algorithm in order to assign each event by its extracted features to one of the three classes namely Fall, near Fall and Naturalistic riding. Regarding the fall detection problem, it is formulated as a sequential anomaly detection problem. The Multivariate CUmulative SUM (MCUSUM) control chart was applied on the data collected from sensors mounted on the motorcycle. The obtained results on a real database have shown the effectiveness of the proposed methodology for both riding pattern recognition and critical riding events detection problemsL'objectif de cette thèse est de développer des outils d'analyse de données recueillies sur les deux roues motorisés (2RMs). Dans ce cadre, des expérimentations sont menées sur des motos instrumentés dans un contexte de conduite réelle incluant à la fois des conduites normales dites naturelles et des conduites à risques (presque chute et chute). Dans la première partie de la thèse, des méthodes d'apprentissage supervisé ont été utilisées pour la classification de situations de conduite d'un 2RM. Les approches développées dans ce contexte ont montré l'intérêt de prendre en compte l'aspect temporel des données dans la conduite d'un 2RM. A cet effet, nous avons montré l'efficacité des modèles de Markov cachés. La seconde partie de cette thèse porte sur le développement d'outils de détection et de classification hors ligne des évènements critiques de conduite, ainsi que, la détection en ligne des situations de chute d'un 2RM. L'approche proposée pour la détection hors ligne des évènements critiques de conduite repose sur l'utilisation d'un modèle de mélange de densités gaussiennes à proportions logistiques. Ce modèle sert à la segmentation non supervisée des séquences de conduite. Des caractéristiques extraites du paramètre du modèle de mélange sont utilisées comme entrées d'un classifieur pour classifier les évènements critiques. Pour la détection en ligne de chute, une méthode simple de détection séquentielle d'anomalies basée sur la carte de contrôle MCUSUM a été proposée. Les résultats obtenus sur une base de données réelle ont permis de montrer l'efficacité des méthodologies proposées à la fois pour la classification de situations de conduite et à la détection des évènements critiques de conduit

Deep HMResNet Model for Human Activity-Aware Robotic Systems

Endowing the robotic systems with cognitive capabilities for recognizing daily activities of humans is an important challenge, which requires sophisticated and novel approaches. Most of the proposed approaches explore pattern recognition techniques which are generally based on hand-crafted features or learned features. In this paper, a novel Hierarchal Multichannel Deep Residual Network (HMResNet) model is proposed for robotic systems to recognize daily human activities in the ambient environments. The introduced model is comprised of multilevel fusion layers. The proposed Multichannel 1D Deep Residual Network model is, at the features level, combined with a Bottleneck MLP neural network to automatically extract robust features regardless of the hardware configuration and, at the decision level, is fully connected with an MLP neural network to recognize daily human activities. Empirical experiments on real-world datasets and an online demonstration are used for validating the proposed model. Results demonstrated that the proposed model outperforms the baseline models in daily human activity recognition.Comment: Presented at AI-HRI AAAI-FSS, 2018 (arXiv:1809.06606

Powered Two Wheelers riding patterns classification and critical events recognition

L'objectif de cette thèse est de développer des outils d'analyse de données recueillies sur les deux roues motorisés (2RMs). Dans ce cadre, des expérimentations sont menées sur des motos instrumentés dans un contexte de conduite réelle incluant à la fois des conduites normales dites naturelles et des conduites à risques (presque chute et chute). Dans la première partie de la thèse, des méthodes d'apprentissage supervisé ont été utilisées pour la classification de situations de conduite d'un 2RM. Les approches développées dans ce contexte ont montré l'intérêt de prendre en compte l'aspect temporel des données dans la conduite d'un 2RM. A cet effet, nous avons montré l'efficacité des modèles de Markov cachés. La seconde partie de cette thèse porte sur le développement d'outils de détection et de classification hors ligne des évènements critiques de conduite, ainsi que, la détection en ligne des situations de chute d'un 2RM. L'approche proposée pour la détection hors ligne des évènements critiques de conduite repose sur l'utilisation d'un modèle de mélange de densités gaussiennes à proportions logistiques. Ce modèle sert à la segmentation non supervisée des séquences de conduite. Des caractéristiques extraites du paramètre du modèle de mélange sont utilisées comme entrées d'un classifieur pour classifier les évènements critiques. Pour la détection en ligne de chute, une méthode simple de détection séquentielle d'anomalies basée sur la carte de contrôle MCUSUM a été proposée. Les résultats obtenus sur une base de données réelle ont permis de montrer l'efficacité des méthodologies proposées à la fois pour la classification de situations de conduite et à la détection des évènements critiques de conduiteThis thesis aims to develop framework tools for analyzing and understanding the riding of Powered Two Wheelers (PTW). Experiments are conducted using instrumented PTW in real context including both normal (naturalistic) riding behaviors and critical riding behaviors (near fall and fall). The two objectives of this thesis are the riding patterns classification and critical riding events detection. In the first part of this thesis, a machine-learning framework is used for riding pattern recognition problem. Therefore, this problem is formulated as a classification task to identify the class of riding patterns. The approaches developed in this context have shown the interest to take into account the temporal aspect of the data in PTW riding. Moreover, we have shown the effectiveness of hidden Markov models for such problem. The second part of this thesis focuses on the development of the off-line detection and classification of critical riding events tools and the on-line fall detection. The problem of detection and classification of critical riding events has been performed towards two steps: (1) the segmentation step, where the multidimensional time of data were modeled and segmented by using a mixture model with quadratic logistic proportions; (2) the classification step, which consists in using a pattern recognition algorithm in order to assign each event by its extracted features to one of the three classes namely Fall, near Fall and Naturalistic riding. Regarding the fall detection problem, it is formulated as a sequential anomaly detection problem. The Multivariate CUmulative SUM (MCUSUM) control chart was applied on the data collected from sensors mounted on the motorcycle. The obtained results on a real database have shown the effectiveness of the proposed methodology for both riding pattern recognition and critical riding events detection problem

Classification de situations de conduite et détection des événements critiques d'un deux roues motorisé

This thesis aims to develop framework tools for analyzing and understanding the riding of Powered Two Wheelers (PTW). Experiments are conducted using instrumented PTW in real context including both normal (naturalistic) riding behaviors and critical riding behaviors (near fall and fall). The two objectives of this thesis are the riding patterns classification and critical riding events detection. In the first part of this thesis, a machine-learning framework is used for riding pattern recognition problem. Therefore, this problem is formulated as a classification task to identify the class of riding patterns. The approaches developed in this context have shown the interest to take into account the temporal aspect of the data in PTW riding. Moreover, we have shown the effectiveness of hidden Markov models for such problem. The second part of this thesis focuses on the development of the off-line detection and classification of critical riding events tools and the on-line fall detection. The problem of detection and classification of critical riding events has been performed towards two steps: (1) the segmentation step, where the multidimensional time of data were modeled and segmented by using a mixture model with quadratic logistic proportions; (2) the classification step, which consists in using a pattern recognition algorithm in order to assign each event by its extracted features to one of the three classes namely Fall, near Fall and Naturalistic riding. Regarding the fall detection problem, it is formulated as a sequential anomaly detection problem. The Multivariate CUmulative SUM (MCUSUM) control chart was applied on the data collected from sensors mounted on the motorcycle. The obtained results on a real database have shown the effectiveness of the proposed methodology for both riding pattern recognition and critical riding events detection problemsL'objectif de cette thèse est de développer des outils d'analyse de données recueillies sur les deux roues motorisés (2RMs). Dans ce cadre, des expérimentations sont menées sur des motos instrumentés dans un contexte de conduite réelle incluant à la fois des conduites normales dites naturelles et des conduites à risques (presque chute et chute). Dans la première partie de la thèse, des méthodes d'apprentissage supervisé ont été utilisées pour la classification de situations de conduite d'un 2RM. Les approches développées dans ce contexte ont montré l'intérêt de prendre en compte l'aspect temporel des données dans la conduite d'un 2RM. A cet effet, nous avons montré l'efficacité des modèles de Markov cachés. La seconde partie de cette thèse porte sur le développement d'outils de détection et de classification hors ligne des évènements critiques de conduite, ainsi que, la détection en ligne des situations de chute d'un 2RM. L'approche proposée pour la détection hors ligne des évènements critiques de conduite repose sur l'utilisation d'un modèle de mélange de densités gaussiennes à proportions logistiques. Ce modèle sert à la segmentation non supervisée des séquences de conduite. Des caractéristiques extraites du paramètre du modèle de mélange sont utilisées comme entrées d'un classifieur pour classifier les évènements critiques. Pour la détection en ligne de chute, une méthode simple de détection séquentielle d'anomalies basée sur la carte de contrôle MCUSUM a été proposée. Les résultats obtenus sur une base de données réelle ont permis de montrer l'efficacité des méthodologies proposées à la fois pour la classification de situations de conduite et à la détection des évènements critiques de conduit

Automatic Recognition of Gait phases Using a Multiple Regression Hidden Markov Model

International audienceThis paper presents a new approach for automatic recognition of gait phases based on the use of an in-shoe pressure measurement system and a Multiple Regression Hidden Markov Model (MRHMM) that takes into account the sequential completion of the gait phases. Recognition of gait phases is formulated as a multiple polynomial regression problem in which each phase, called a segment, is modelled using an appropriate polynomial function. The MRHMM is learned in an unsupervised manner to avoid manual data labelling, which is a laborious, time-consuming task that is subject to potential errors, particularly for large amounts of data. To evaluate the efficiency of the proposed approach, several performance metrics for classification are used: accuracy, F-measure, recall and precision. Experiments conducted with 5 subjects during walking show the potential of the proposed method to recognize gait phases with relatively high accuracy. The proposed approach outperforms standard unsupervised classification methods (GMM, k-Means and HMM) while remaining competitive with respect to standard supervised classification methods (SVM, RF and k-NN)

Powered Two-Wheelers Critical Events Detection and Recognition Using Data-Driven Approaches

Driving errors are considered to be the greatest contributory cause in all road accidents and an important contributory cause of most fatal accidents. This is particularly the case for the users of powered two-wheeled vehicles (PTWs), perhaps because PTW riders play a greater role in the control of their vehicles' stability than four-wheeled vehicle drivers. Thus, observing and analyzing the evolution of riders' behavior in a real-life context is an important step in the identification of theroad environment characteristics that constitute a risk factor for PTW riders. A relevant research issue in naturalistic studies is related to the detection and identification of critical riding events from among the vast amount of data recorded during the experiment. In this paper, two approaches were used to automatically detect such critical riding events. First, we formalized this problem in terms of detecting changes in the mean and variance of the signals generated by the acceleration and angular velocity sensors. For this purpose, two steps were performed: 1) a data segmentation and feature extraction step in which the multidimensional time series of accelerometer and angular velocity data were segmented and modeled using a Gaussian mixture model with quadratic logistic proportions; and 2) a classification step in which each detected segment was assigned to the appropriate riding sequence, whether 'naturalistic' or 'critical' (i.e., fall or near fall), using the k-nearest neighbor algorithm. The second approach was based on online fall detection. This methodology used control charts (a multivariate cumulative sum), an approach that has been traditionally employed for sequential detection. These two algorithms were applied to a database composed of data from a real-life driving experiment. The obtained results show the effectiveness of both methodologies.Driving errors are considered to be the greatest contributory cause in all road accidents and an important contributory cause of most fatal accidents. This is particularly the case for the users of powered two-wheeled vehicles (PTWs), perhaps because PTW riders play a greater role in the control of their vehicles' stability than four-wheeled vehicle drivers. Thus, observing and analyzing the evolution of riders' behavior in a real-life context is an important step in the identification of theroad environment characteristics that constitute a risk factor for PTW riders. A relevant research issue in naturalistic studies is related to the detection and identification of critical riding events from among the vast amount of data recorded during the experiment. In this paper, two approaches were used to automatically detect such critical riding events. First, we formalized this problem in terms of detecting changes in the mean and variance of the signals generated by the acceleration and angular velocity sensors. For this purpose, two steps were performed: 1) a data segmentation and feature extraction step in which the multidimensional time series of accelerometer and angular velocity data were segmented and modeled using a Gaussian mixture model with quadratic logistic proportions; and 2) a classification step in which each detected segment was assigned to the appropriate riding sequence, whether 'naturalistic' or 'critical' (i.e., fall or near fall), using the k-nearest neighbor algorithm. The second approach was based on online fall detection. This methodology used control charts (a multivariate cumulative sum), an approach that has been traditionally employed for sequential detection. These two algorithms were applied to a database composed of data from a real-life driving experiment. The obtained results show the effectiveness of both methodologies

Hybrid approach for Human Activity Recognition by Ubiquitous Robots

International audienceOne of the main objectives of ubiquitous robots is to proactively provide context-aware intelligent services to assist humans in their professional or daily living activities. One of the main challenges is how to automatically obtain a consistent and correct description of human context such as location, activities, emotions, etc. In this paper, a new hybrid approach for reasoning on the context is proposed. This approach focuseson human activity recognition and consists of machine-learning algorithms, an expressive ontology representation, and a reasoningsystem. The latter allows detecting the inconsistencies that may appear during the machine learning phase. The proposed approach can also correct automatically these inconsistencies by considering the context of the ongoing activity. The obtained results on the Opportunity dataset demonstrate the feasibility of the proposed method to enhance the performance of human activity recognition

Data-driven based approach to aid Parkinson’s disease diagnosis

International audienceThis article presents a machine learning methodology for diagnosing Parkinson’s disease(PD) based on the use of vertical Ground Reaction Forces (vGRFs) data collected from the gaitcycle. A classification engine assigns subjects to healthy or Parkinsonian classes. The diagnosisprocess involves four steps: data pre-processing, feature extraction and selection, data classificationand performance evaluation. The selected features are used as inputs of each classifier. Featureselection is achieved through a wrapper approach established using the random forest algorithm. Theproposed methodology uses both supervised classification methods including K-nearest neighbour(K-NN), decision tree (CART), random forest (RF), Naïve Bayes (NB), support vector machine(SVM) and unsupervised classification methods such as K-means and the Gaussian mixture model(GMM). To evaluate the effectiveness of the proposed methodology, an online dataset collectedwithin three different studies is used. This data set includes vGRFs measurements collected fromeight force sensors placed under each foot of the subjects. 93 patients suffering from Parkinson’sdisease and 72 healthy subjects participated in the experiments. The obtained performances arecompared with respect to various metrics including accuracy, precision, recall and F-measure. Theclassification performance evaluation is performed using the Leave-one-out cross validation. Theresults demonstrate the ability of the proposed methodology to accurately differentiate betweenPD subjects and healthy subjects. For the purpose of validation, the proposed methodology isalso evaluated with an additional dataset including subjects with neurodegenerative diseases(Amyotrophic Lateral Sclerosis (ALS) and Huntington’s disease (HD)). The obtained results showthe effectiveness of the proposed methodology to discriminate PD subjects from subjects with otherneurodegenerative diseases with a relatively high accuracy

Hybrid approach for Human Activity Recognition by Ubiquitous Robots

International audienceOne of the main objectives of ubiquitous robots is to proactively provide context-aware intelligent services to assist humans in their professional or daily living activities. One of the main challenges is how to automatically obtain a consistent and correct description of human context such as location, activities, emotions, etc. In this paper, a new hybrid approach for reasoning on the context is proposed. This approach focuseson human activity recognition and consists of machine-learning algorithms, an expressive ontology representation, and a reasoningsystem. The latter allows detecting the inconsistencies that may appear during the machine learning phase. The proposed approach can also correct automatically these inconsistencies by considering the context of the ongoing activity. The obtained results on the Opportunity dataset demonstrate the feasibility of the proposed method to enhance the performance of human activity recognition